Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks
Tomasz Serafin and
No WORMS/23/01, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology
We propose a novel electricity price forecasting model tailored to intraday markets with continuous trading. It is based on distributional deep neural networks with Johnson SU distributed outputs. To demonstrate its usefulness, we introduce a realistic trading strategy for the economic evaluation of ensemble forecasts. Our approach takes into account forecast errors in wind generation for four German TSOs and uses the intraday market to resolve imbalances remaining after day-ahead bidding. We argue that the economic evaluation is crucial and provide evidence that the better performing methods in terms of statistical error metrics do not necessarily lead to higher trading profits.
Keywords: Intraday electricity market; Probabilistic forecast; Path forecast; Prediction bands; Trading strategy; Neural networks (search for similar items in EconPapers)
JEL-codes: C22 C32 C45 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 13 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-for and nep-mac
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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_23_01.pdf Original version, 2023 (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:ahh:wpaper:worms2301
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